1. Field of the Invention
The present invention relates to an image processing device, an image processing method, and an image processing program for extracting a specific region based on the density values of pixels in an input image.
2. Description of the Related Art
Hitherto, in the medical field, processing for extracting and displaying a specific region such as a lesion region or an internal organ region within a medical image is executed in order to provide an image exhibiting high diagnostic performance.
In P. Therasse et al., “New Guidelines to Evaluate the Response to Treatment in Solid Tumors”, Journal of the National Cancer Institute, Vol. 92, No. 3, pp. 205-216, 2000 (Non Patent Literature 1), it is recommended that the long diameter of a lesion region and the longest distance in a direction orthogonal to the long diameter be used as an index for determining a deteriorating situation of a disease and treatment effect in treatment of a tumor. In recent years, in order to accurately determine the treatment effect of a disease, there is also a demand to evaluate the size of a specific region such as a tumor region with more accuracy, and as disclosed in S. Iwano et al., “Semi-automatic Volumetric Measurement of Lung Cancer Using Multi-detector CT: Effects of Nodule Characteristics”, Academic Radiology, Vol. 16, No. 10, pp. 1179-1186, 2009 (Non Patent Literature 2), there is also proposed a method using an area or a volume of a lesion region as an index for determining the treatment effect of a disease.
As a method of extracting the specific region within the image, as disclosed in Japanese Unexamined Patent Publication No. 2007-307358 (Patent Literature 1) or the like, there is a method involving causing a user to designate an arbitrary point in the lesion region within the medical image, setting a discrimination region with reference to the designated point, and evaluating whether each pixel in the region is a pixel indicating a contour of the lesion region, to determine the contour of the lesion region based on evaluation results. Here, it is evaluated whether each pixel is a pixel indicating the contour based on an evaluation function found in advance by conducting machine learning using a large number of sample images including lesion regions whose contours are known.